With times development, types and quantities of products that can meet people's needs are gradually increasing, and product quality also becomes a major concern for users and enterprises. Especially for the enterprises, the product quality is fundamental to the enterprises. Therefore, lowering a defect rate of the products is of vital importance for the enterprises. A cause of a product defect is mainly a production technique of a product, including design of the product, quality of a used material, a manufacturer capability, and the like. Therefore, for the enterprises, if the defect rate of the products needs to be lowered, the production technique of the product needs to be analyzed and improved, so as to improve the product quality.
For each product, there is a record of information about the product in various aspects, such as a source of a raw material, production information, test information, transportation information, and usage information. When a defect or a fault of a type occurs in a product in a production or use process, a factor that causes this type of a defect or a fault is somehow correlated with the recorded information about the product.
In the prior art, a method for predicting a defect of a faulty product is provided and is specifically as follows: a single decision tree is generated by using recorded information about a product in which a fault has occurred and by using a decision tree-based classification algorithm; and in this way, when a fault occurs in the product, a defect of the faulty product can be predicted according to the generated decision tree. However, when there are multiple classification labels of the recorded information about the product in which a fault has occurred, the single decision tree generated by using the decision tree-based classification algorithm may easily cause overfitting or underfitting, and therefore the defect prediction cannot be performed. Therefore, when a defect or a fault occurs in a product, how to quickly locate a fault point and find a fault cause has become a focus in industry research.